Short-term traffic forecasting is one of the key functions in Intelligent Transportation System (ITS). Recently, deep learning is drawing more attention in this field. However, how to develop a deep learning based traffic forecasting model that can dynamically extract explainable spatial correlations from traffic data is still a challenging issue. The difficulty mainly comes from the inconsistency between static model structures and the dynamic evolution of traffic conditions. To overcome this difficulty, we proposed a novel multistep speed forecasting model, Dynamic Graph Filters Networks (DGFN). The major contribution is that the regular pixel-wise dynamic convolution is extended to graph topology. DGFN has a simple recurrent cell structure where local area-wide graph convolutional kernels are dynamically computed from varying inputs. Experiments on ring freeways show that DGFN is able to precisely predict short-term evolution of traffic speed. Furthermore, we theoretically explain why DGFN is not a pure “black-box”, but a “gray-box” model that actually reduces entangled spatial and temporal features into one component representing dynamic spatial correlations. It permits tracking real-time interactions among adjacent links. DGFN has the potential to relate trained parameters in deep learning models with physical traffic variables.
|Title of host publication||2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)|
|Number of pages||6|
|Publication status||Published - 2020|
|Event||The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020) - Rhodes, Greece|
Duration: 20 Sep 2020 → 23 Sep 2020
|Name||2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020|
|Conference||The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020)|
|Period||20/09/20 → 23/09/20|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.